Abstract

We present a new differential evolution algorithm for multiobjective optimization
controlled by the self-adaptation mechanism introduced in evolution stategies.

Algorithm design is presented with mathematically formal notation
of algorithm's main parts and their assembly.
The algorithm is described using a pseudocode.
Computational complexity of the algorithm is given and some empirical
measurement are given for evidence.
Self-adaptation dynamics of control parameters is also studied.

State of the art test problems and quality indicators from literature for performance
assessment of multiobjective optimization algorithms are listed.
Using these, performance assessments of the algorithm are obtained showing numerous statistically significant improvements.
Obtained results with the algorithm are also compared with related
algorithms and statistically significant differences of the compared algorithms
are pointed out on empirical results.